49 citations · 57 across the 5 of their papers we have counts for
7 papers
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives
Eva Vanmassenhove, Chris Emmery, Dimitar Shterionov
Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive…
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
Eva Vanmassenhove, Dimitar Shterionov, Matthew Gwilliam
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The ampl…
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation
Xabier Soto, Dimitar Shterionov, Alberto Poncelas +1
Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation. Combining backtran…
Combining SMT and NMT Back-Translated Data for Efficient NMT
Alberto Poncelas, Maja Popovic, Dimitar Shterionov +2
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training…
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
Eva Vanmassenhove, Dimitar Shterionov, Andy Way
This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show ho…
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
Eva Vanmassenhove, Amit Moryossef, Alberto Poncelas +2
We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more "traditio…